Barcode Reconstruction Using Sequence Alignment Matrices
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Solution Overview
Problem
Advanced Code Reconstruction (ACR) methods for barcode readers face challenges in accurately reconstructing barcode symbols due to split and merge sequences, leading to poor performance and incorrect reconstructions.
Innovation Solution
The use of Sequence Alignment Matrices (SAMs) to align and score barcode signal sequences, allowing for the identification of optimal alignments and reconstruction of barcode symbols by summing scores across diagonals, excluding non-matching elements, and employing a custom quality index measure for improved readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional heuristic rules are used for matching partial barcode signals, then the reconstruction process can identify aligned elements, but the performance deteriorates under split and merge sequence conditions leading to incorrect reconstructions
Solution Approach 1:
The patent segments the barcode signal into discrete elements (bars and spaces) and represents them as sequences in a matrix structure. Each element is individually scored and aligned, allowing the system to handle split and merge conditions by treating them as separate element-level operations rather than requiring holistic pattern matching. This segmentation enables accurate reconstruction even when sequences are fragmented or merged.
Solution Approach 2:
The patent introduces a sequence alignment matrix as an intermediary structure between the received partial barcode signal and the reconstructed barcode symbol. This matrix serves as a mediator that systematically compares and aligns elements from different sequences, scoring potential alignments and resolving ambiguities. The matrix intermediary handles split and merge conditions by providing a structured framework for evaluating all possible element correspondences.
2Measurement precision
If complex heuristic rules are employed for element-by-element matching, then alignment identification is possible, but the device complexity increases
Solution Approach 1:
The patent transforms the matching problem from a complex heuristic rule-based system into a parameter-driven scoring system. Each matrix cell is assigned a numerical score based on element compatibility, and alignments are determined by maximizing the sum of scores along diagonals. This parameter change from qualitative heuristic rules to quantitative scoring simplifies the algorithm while maintaining or improving alignment precision.
Solution Approach 2:
The patent elevates the matching problem from one-dimensional sequence comparison to two-dimensional matrix analysis. By representing partial signals and reconstructed symbols as rows and columns in a matrix, the system can visualize and compute alignments across multiple dimensions simultaneously. This dimensional transformation enables systematic evaluation of all possible element correspondences without requiring complex heuristic rules.
3Reliability
If multiple partial signals are acquired and reconstructed, then complete barcode symbol recovery is possible, but the time required for reconstruction increases
Solution Approach 1:
The patent performs preliminary organization of partial barcode signals into a structured matrix format before executing the alignment computation. By pre-processing the input signals and arranging elements in systematic rows and columns, the system prepares the data structure to enable efficient scoring and diagonal summation operations. This preliminary action reduces the computational burden during the actual reconstruction phase.
Solution Approach 2:
The patent maintains continuous useful action by systematically processing all elements in the matrix without interruption. The scoring and diagonal summation operations proceed continuously across the entire matrix structure, evaluating all possible alignments in a unified computational pass. This continuous processing approach maximizes the utilization of acquired partial signals while minimizing redundant computations.
Data Source
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AI summary
Systems, methods, and computer-readable storage media are provided reconstructing barcode signals utilizing sequence alignment matrices. A barcode signal is received that is associated with a portion of a barcode symbol and includes a sequence of bar elements and space elements in alternating order. A sequence alignment matrix (SAM) is built such that each row represents an element of an already reconstructed portion of the barcode symbol, each column represents an element of the received barcode signal sequence, and the potential alignments are placed on a plurality of diagonals thereof. A score is assigned to each matrix square that includes an element of the received barcode signal sequence and a diagonal score is calculated for each of the plurality of diagonals by summing the scores for each matrix square respectively comprising each of the plurality of diagonals.